System and Method for Identifying and Attenuating Mental Health Deterioration

ABSTRACT

A method for determining a state of mind of a user may include receiving one or more strings of characters composed by the user, and determining, by a processing device, the state of mind of the user by processing the one or more strings of characters. The processing of the one or more strings of characters may include identifying similarities of the one or more strings of characters with other strings of characters indicative of the state of mind. The method may also include determining, based on the one or more strings of characters, a severity of the state of mind of the user.

TECHNICAL FIELD

This disclosure relates to computational linguistics. More specifically,this disclosure relates to a system and method for using computationallinguistics to identify and attenuate mental health deterioration.

BACKGROUND

People have struggled with depression since the beginning of recordedhistory. Anthropological studies suggest that hunter-gatherer groupsexhibited similar depressive symptoms as the modern-day population does,with similar reactions to similar types of issues, and similar resultingbiological manifestations. Depression, anxiety, and other deterioratedstates of mental health are often treated in similar fashion as discreteillnesses, where diagnoses and resulting treatments are primarilypredicated on whether or not an observer, or a mechanical method ofobservation, suggests a person is in a specific state of mind: depressedor not depressed. However, depression is not a typical illness.Depression is a probabilistic tendency to exhibit any of a wide range ofinterrelated symptoms in response to environmental adversity. Simplystated, depression is more akin to an oscillation between two mentalpoles: happy and sad.

SUMMARY

Representative embodiments set forth herein disclose various techniquesfor enabling a system and method for electronic assignment of issuesbased on measured and/or forecasted capacity of human resources.

In one embodiment, a method for determining a state of mind of a usermay include receiving one or more strings of characters composed by theuser, and determining, by a processing device, the state of mind of theuser by processing the one or more strings of characters. The processingof the one or more strings of characters may include identifyingsimilarities of the one or more strings of characters with other stringsof characters indicative of the state of mind. The method may alsoinclude determining, based on the one or more strings of characters, aseverity of the state of mind of the user. In some embodiments, anintervention may be performed electronically, in real-time or nearreal-time, based on the state of mind of the user and/or the severity ofthe state of mind of the user.

In some embodiments, a tangible, non-transitory computer-readable mediumstoring instructions that, when executed, cause a processing device toperform one or more of the operations described above. In someembodiments, a system may include a memory storing instructions and aprocessor communicatively coupled to the memory. The processor mayexecute the instructions to perform one or more of the operationsdescribed above.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of example embodiments, reference will now bemade to the accompanying drawings in which:

FIG. 1 illustrates a high-level component diagram of an illustrativesystem architecture according to certain embodiments of this disclosure;

FIG. 2 illustrates an example block diagram of a cloud-based computingsystem receiving text, indications of a state of mind of a user, and aseverity of the state of mind according to certain embodiments of thisdisclosure;

FIG. 3 illustrates an example block diagram of receiving additional textof the user according to certain embodiments of this disclosure;

FIG. 4 illustrates an example block diagram of preforming one or moreinterventions according to certain embodiments of this disclosure;

FIG. 5 illustrates an example block diagram of receiving text fromspoken words in a video according to certain embodiments of thisdisclosure;

FIG. 6 illustrates example operations of a method for performing anintervention based on a state of mind of a user and a severity of mindof the user according to certain embodiments of this disclosure; and

FIG. 7 illustrates an example computer system.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different entities may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments. The phrase “at least one of,” when used witha list of items, means that different combinations of one or more of thelisted items may be used, and only one item in the list may be needed.For example, “at least one of: A, B, and C” includes any of thefollowing combinations: A, B, C, A and B, A and C, B and C, and A and Band C. In another example, the phrase “one or more” when used with alist of items means there may be one item or any suitable number ofitems exceeding one.

Moreover, various functions described below can be implemented orsupported by one or more computer programs, each of which is formed fromcomputer readable program code and embodied in a computer readablemedium. The terms “application” and “program” refer to one or morecomputer programs, software components, sets of instructions,procedures, functions, objects, classes, instances, related data, or aportion thereof adapted for implementation in a suitable computerreadable program code. The phrase “computer readable program code”includes any type of computer code, including source code, object code,and executable code. The phrase “computer readable medium” includes anytype of medium capable of being accessed by a computer, such as readonly memory (ROM), random access memory (RAM), a hard disk drive, acompact disc (CD), a digital video disc (DVD), solid state drives(SSDs), flash memory, or any other type of memory. A “non-transitory”computer readable medium excludes wired, wireless, optical, or othercommunication links that transport transitory electrical or othersignals. A non-transitory computer readable medium includes media wheredata can be permanently stored and media where data can be stored andlater overwritten, such as a rewritable optical disc or an erasablememory device.

Definitions for other certain words and phrases are provided throughoutthis patent document. Those of ordinary skill in the art shouldunderstand that in many if not most instances, such definitions apply toprior as well as future uses of such defined words and phrases.

DETAILED DESCRIPTION

Approaches to identifying a trajectory of a mental health eventtypically take form in one of two ways: (1) human personality diagnosesconducted by an expert in an attempt to identify schema usingtheoretical and practical knowledge, and (2) assessments built onfindings from individual cases and analyses that are presented in theform of an encouraged literary exercise or a questionnaire that can beapplied to a wide audience. However, these approaches have severalshortcomings. For example, ignoring the idea that adaptive failures runalong a continuum may contribute to these approaches being lesseffective, which in return, promotes an imbalanced or inaccuratecare-delivery regimen. A negligible portion of the population remains ina single, consistent state of happiness. Moreover, the psychoanalyticperspective of these approaches view mental disorders as one largeprocess that is amendable to a single treatment. These approaches voidthe possibility for real-time identification, thereby making it nearimpossible to intervene at an opportune time.

Accordingly, embodiments of the present disclosure may refer toidentifying a mental state of a user and a severity of the mental stateand performing an intervention in real-time or near real-time. Real-timeor near real-time may refer to performing an action (e.g., intervention,transmitting data (text, characters, words, video, etc.) within afraction of a second. In some embodiments, real-time text may refer totext that is transmitted while it is being typed or created, withcharacters being sent immediately once typed, and also displayed and/orprocessed immediately to any receiving computing device. In someembodiments, real-time messaging may refer to messages that arecompletely composed and transmitted immediately (within a fraction of asecond) upon a user selecting to send the message or some triggeringfactor (e.g., a threshold delay period where the user stops enteringtext, the user selects to save the message, etc.).

Computational linguistics may refer to an interdisciplinary fieldconcerned with statistical/rule-based processing of natural languagethrough computation. In some embodiments, computational linguistics maybe used for accurate and effective personality and health analysis forthe purpose of identifying a particular state of mind and/or severity ofthe state of mind and quickly applying an appropriate intervention. Insome embodiments, artificial intelligence techniques may be used, suchas machine learning models implementing neural networks, or the like,that are trained to receive strings of characters entered or spoken bythe user and determine a state of mind of the user, determining aseverity of the state of mind of the user, and/or perform anintervention based on the state of mind of the user and/or the severityof the state of mind of the user.

The machine learning models may be trained based on input from numeroususers. For example, a user may use an application to enter text or speakwords and may specify their state of mind and the severity of theirstate of mind at the time at which they entered the text or spoke thewords. The state of mind may be any suitable state of mind such ashappy, sad, mad, frustrated, playful, tired, worried, anxious, etc.Accordingly, the machine learning models may be trained with a “groundtruth” or baseline of a correlation of the way in which the users typeor speak and their mental states and/or severities of their mentalstates. In some embodiments, a corpus of training data including stringsof characters of users and labeled with states of mind and/or severitiesof states of mind of the user when the strings of characters werecreated. The corpus of training data may be obtained from one or morerepositories that harbor mental states of users. The machine learningmodels may perform natural language processing and distributionalsemantics to process written language and/or spoken languagehistorically and/or in real-time to assess the state of mind and/orseverity of the state of mind. Further, the disclosed techniques maycause a minor to major intervention predicated on the mental stateand/or severity of the mental state of the user.

In some embodiments, the interventions may be performed in a digitalrealm, a physical environment realm, and/or the like. For example, theinterventions may include: (i) causing a color of a display screen of acomputing device of the user to be altered, (ii) transmitting a firstmessage to a computing device of a third party, where the first messagerecommends contacting the user, (iii) causing a prompt to be presentedon the computing device of the user, where the prompt recommends theuser stand up, walk, or briefly meditate, (iv) causing the computingdevice of the user to connect to a telephonic-health service, (v)transmitting a second message to an emergency service, where the secondmessage indicates an event is likely to occur, and/or (vi) causing anelectronic device to change a parameter (e.g., smart light changes coloremitted, brightness, or both; smart thermostat changes temperature;speaker plays music; speaker emits a phrase).

In some embodiments, the change in the state of mind of the user and/orthe severity of the state of mind of the user over time may be tracked.If the severity of the state of mind changes a threshold amount (e.g.,2-8 levels) in less than a threshold amount of time (e.g., less than anhour, a few hours, a day, a few days, etc.), then a major interventionmay be performed. For example, if the user is in a sad state of mind andthe severity changes from a 1 to a 9 within a day, then a majorintervention may be performed. If the severity of the state of mindchanges less than the threshold amount (e.g., 1 level) in a thresholdamount of time (e.g., more than an hour, a few hours, a day, a few days,etc.), then a minor intervention may be performed. It should be notedthat the threshold amount of severity levels and the severity amount oftime may be configurable to any suitable values.

The disclosed embodiments may provide various technical advantages. Forexample, the disclosed embodiments receive data from multiple differentdata feeds (e.g., any application executed on the computing device ofthe user where the user enters text: email applications, word processingapplications, spreadsheet applications, note applications, social mediaapplications, internet websites, etc.). The disclosed techniques mayalso quickly and efficiently process the respective data individually orin combination to determine a state of mind of a user and/or a severityof the user to perform an intervention in real-time or near real-time tointervene at an opportune moment. Intervening at the opportune momentmay prevent a user from performing a harmful act to their self or othersand/or may alter the mental state of the user to change their mentalstate and/or lessen/increase the severity of their mental state. Thedisclosed embodiments may improve a user's experience with a computingdevice due to the interventions being performed at selected momentswhere the users are feeling particularly sad, depressed, and/orfrustrated. The interventions may enable a computing device of acloud-based computing system to control various other computing device.

FIG. 1 illustrates a high-level component diagram of an illustrativesystem architecture 100 according to certain embodiments of thisdisclosure. In some embodiments, the system architecture 100 may includea computing device 101, a computing device 102, a cloud-based computingsystem 116, an electronic device 150, and/or a third party database 130that are communicatively coupled via a network 112. As used herein, acloud-based computing system refers, without limitation, to any remoteor distal computing system accessed over a network link. Each of thecomputing device 101, computing device 102, and electronic device 150may include one or more processing devices, memory devices, and networkinterface devices.

The network interface devices of the computing devices 101 and 102 andthe electronic device 150 may enable communication via a wirelessprotocol for transmitting data over short distances, such as Bluetooth,ZigBee, near field communication (NFC), etc. Additionally, the networkinterface devices may enable communicating data over long distances, andin one example, the computing device 101 and/or 102 and the electronicdevice 150 may communicate with the network 112. Network 112 may be apublic network (e.g., connected to the Internet via wired (Ethernet) orwireless (WiFi)), a private network (e.g., a local area network (LAN),wide area network (WAN), virtual private network (VPN)), or acombination thereof.

The computing device 101 may be any suitable computing device, such as alaptop, tablet, smartphone, or computer. The computing device 101 mayinclude a display that is capable of presenting a user interface 105.The computing device 101 may be operated by a third party (e.g., aspouse, a family member, a son, a relative, a sponsor, an emergencyservice employee, or any suitable person besides a user of the computingdevice 102). The user interface 105 may be implemented in computerinstructions stored on a memory of the computing device 101 and executedby a processing device of the computing device 101. The user interface105 may be a stand-alone application that is installed on the computingdevice 101 or may be an application (e.g., website) that executes via aweb browser. The user interface 105 may present various screens,notifications, and/or messages to a user. The screens, notifications,and/or messages may encourage the user of the computing device 101 tocontact the user of the computing device 102 by emailing them, textmessaging them, and/or calling them. The encouragement may specify theuser of the computing device 101 to inquire how the user of thecomputing device 102 is feeling and attempt to cheer up the user of thecomputing device 102. Further, the notification may indicate to anemergency service employee (e.g., dispatcher, police officer, emergencymedical technician, etc.) that a potentially harmful event may occurbased on the determined mental state and/or severity of mental state ofthe user of the computing device 102.

The computing device 102 may execute a third party application 107. Thethird party application 107 may be implemented in computer instructionsstored on the one or more memory devices of the computing device 102 andexecutable by the one or more processing devices of the computing device102. The third party application 107 may be any suitable softwareapplication that is capable of receiving textual input and/or videoinput. For example, the third party application 107 may be a wordprocessing application, spreadsheet application, slideshow application,software development application, animation application, video editingapplication, note taking application, social media application, socialmedia website, web browser, or the like. The third party application 107may interface with an application programming interface 135 (API) of thecloud-based computing system 116. For example, the API 135 may be usedto provide a user interface element (e.g., a text input box and/orfield) that is embedded in a screen of the third party application 107.The third party application 107 may be a bot that is implemented by avirtual meeting platform or any suitable web site that allows users toimplement text and/or video of users talking in real-time. A bot mayrefer to a computer program that performs automated tasks. The automatedtasks may include transmitting text to the cloud-based computing system116, extracting text from spoken words in a video and transmitting thetext to the cloud-based computing system 116, or some combinationthereof.

The computing device 102 may also execute a tracking application 111.The tracking application 111 may be implemented in computer instructionsstored on the one or more memory devices of the computing device 102 andexecutable by the one or more processing devices of the computing device102. The tracking application 111 may be provided by the cloud-basedcomputing system 116. The tracking application 111 may be a keystrokeanalyzer that monitors any program the user uses to enter text. Thetracking application 111 may transmit the text represented by thekeystrokes the user performed in any suitable application to thecloud-based computing system 116. The tracking application 111 maymonitor an amount of time the user is actively using the third partyapplication 107 and transmit that amount of time to the cloud-basedcomputing system 116. The amount of time the user uses the third partyapplication 107 may be considered as a factor when determining a mentalstate of the user and/or a severity of the mental state of the user.

In some embodiments, the cloud-based computing system 116 may includeone or more servers 128 that form a distributed, grid, and/orpeer-to-peer (P2P) computing architecture. Each of the servers 128 mayinclude one or more processing devices, memory devices, data storage,and/or network interface devices. The servers 128 may be incommunication with one another via any suitable communication protocol.The servers 128 may determine the mental state of the user and/or aseverity of the mental state of the user based on strings of charactersentered by the user or extracted from words spoken by the user in avideo. The servers 128 may use one or more machine learning models 154trained to determine the mental state of the user and/or a severity ofthe mental state of the user. The server 128 and/or the machine learningmodels 154 may determine to perform an intervention based on the mentalstate of the user and/or a severity of the mental state of the user.

In some embodiments, the cloud-based computing system 116 may include atraining engine 152 and/or the one or more machine learning models 154.The training engine 152 and/or the one or more machine learning models154 may be communicatively coupled to the servers 128 or may be includedin one of the servers 128. In some embodiments, the training engine 152and/or the machine learning models 154 may be included in the computingdevice 101 and/or 102.

The one or more of machine learning models 154 may refer to modelartifacts created by the training engine 152 using training data thatincludes training inputs and corresponding target outputs (correctanswers for respective training inputs). The training engine 152 mayfind patterns in the training data that map the training input to thetarget output (the answer to be predicted), and provide the machinelearning models 154 that capture these patterns. The set of machinelearning models 154 may comprise, e.g., a single level of linear ornon-linear operations (e.g., a support vector machine [SVM]) or a deepnetwork, i.e., a machine learning model comprising multiple levels ofnon-linear operations. Examples of such deep networks are neuralnetworks including, without limitation, convolutional neural networks,recurrent neural networks with one or more hidden layers, and/or fullyconnected neural networks.

In some embodiments, the training data may include inputs of words(strings of characters), types of words, number of words, misspelledwords, times and/or dates when the words are entered, phrases, facialexpressions, vital signs, financial information, health relatedinformation, or some combination thereof and correlated outputs of amental state and/or a severity of the mental state. The training datamay be obtained from a sample of people using a computer application toenter strings of characters (e.g., a digital diary) describing how theyare feeling or what they are doing and also inputting their mental stateand/or how severe their mental state is. For example, the text may be “Iam very sad” which may be correlated with a sad mental state having ahigh severity (e.g., level 7-10). In other instances, the user may ormay not use certain pronouns at a certain ratio to other types of words,may misspell words, may use certain types of words at a certain ratio toother types of words, may use the computer application at a certain timeof day, make certain facial expressions, and the like that is correlatedwith the mental state and/or severity of mental state specified by theuser. The machine learning models 154 may be trained to use naturallanguage processing and semantic distribution to recognize the stringsof characters received from the computing device 102. In cases wherevideos and/or images are received, the machine learning models 154 maybe trained to use facial character recognition, object characterrecognition, and/or facial expression detection to determine what typeof facial expression the user is making.

The natural language processing may use if-then rules, and/orstatistical models. The statistical models may make soft, probabilisticdecisions based on attaching real-valued weights to each input feature.Such models may have the advantage that they may express relativecertainty of many different possible answers rather than just one,thereby producing more reliable results when such a model is included asa component of a larger system. The learning procedures used duringmachine learning natural language processing may automatically focus onthe most common cases. Further, automatic learning procedures may makeuse of statistical-inference algorithms to produce models that arerobust to unfamiliar input (e.g. containing words or structures thathave not been seen before) and to erroneous input (e.g. with misspelledwords or words accidentally omitted). Further, systems based onautomatically learning the rules can be made more accurate simply bysupplying more input data. The natural language processing may usevarious syntax techniques, such as grammar induction, lemmatization,morphological segmentation, part-of-speech tagging, parsing, sentencebreaking, stemming, word segmentation, and/or terminology extraction.Also, the natural language processing may use various semantics, such aslexical semantics, distributional semantics, machine translation, namedentity recognition, natural language generation, natural languageunderstanding, optical character recognition, question answering,recognizing textual entailment, relationship extraction, sentimentanalysis, topic segmentation, word sense disambiguation, and the like.Further, natural language processing may perform the following discoursetechniques: automatic summarization, coreference resolution, discourseanalysis, and the like. The natural language processing may also performthe following speech techniques: speech recognition, speechsegmentation, text-to-speech, dialogue, and the like.

The machine learning models 154 may be trained with the training data toperform an intervention based on the determined mental state and/orseverity of the mental state. For example, if the mental state of theuser is sad and the severity is at a threshold level (e.g., 7-10), thena major intervention may be performed, such as contacting emergencyservices to notify that a harmful or bad event may occur soon. If themental state of the user is sad and the severity is below the thresholdlevel (e.g., less than a 7), then a minor intervention may be performed.

In some embodiments, the trained machine learning model 154 may receivean input of strings of characters (e.g., entered by the user using thethird party application 107 and/or extracted from spoken words in avideo) and/or facial images depicting facial expressions, and output themental state of the user and/or the severity of the user. In someembodiments, the machine learning models 60 are linked such that theiroutputs are used as inputs to one another. For example, the mental stateoutput by a first machine learning model 154 may be input into a secondmachine learning model 154 that outputs the severity of the mentalstate.

In some embodiments, the cloud-based computing system 116 may include adatabase 129. The third party database 130 may store data pertaining toobservations determined by the machine learning models 154. Theobservations may pertain to the mental state and/or severity of mentalstates of certain users. The observations may be stored by the database129 over time to track the degradation and/or improvement of the mentalstate of the user. Further, the observations may include indications ofwhich types of interventions are successful in improving the mentalstate and/or lowering the severity of a mental state when a user has aparticular mental state at a particular severity. In some embodiments,the actual text received from the computing device 102 may not be storedby the database 129. The database 130 may store data pertaining to acorpus of strings of characters and correlated mental states and/orseverity levels of the mental states of users based on the observations.The training data used to train the machine learning models 154 may bestored in the database 129.

In some embodiments, the cloud-based computing system 116 may include anapplication programming interface (API) 135 that communicatively couplesto the third party database 130 via the network 112. The API 135 may beimplemented as computer instructions stored on one of the servers 128and executed by a processing device of one of the servers 128. The thirdparty database 130 may store data pertaining to a corpus of strings ofcharacters and correlated mental states and/or severity levels of themental states of users. In some embodiments, the third party database130 may not store the actual text entered by users. For example, theentity may be a police department, a medical facility, a psychiatricfacility, a research facility, or the like. The data in the third partydatabase 130 may be harvested from computing devices of users of theentity using tracking applications and/or survey applications. The API135 may extract the data from the third party database 130 to performthe techniques disclosed herein. The training data used to train themachine learning models 154 may be stored in the third party database130.

FIG. 2 illustrates an example block diagram for a cloud-based computingsystem 116 receiving text 200, indications of a state of mind 202 of auser, and a severity 204 of the state of mind of the user according tocertain embodiments of this disclosure. As depicted, the user is usingthe computing device 102 and a third party application 107 that may beimplementing a text input box of the API 135. The user in the depictedexample may be a police officer entering an incident report. The usertyped “A burglary was reported at 10 PM at the corner of Maple Streetand Elm Street. We talked to an eye witness who indicated the suspectwas wearing a baseball cap and had a crowbar that he used to break openthe front door of a house. Thankfully, there was no one hurt at thescene of the crime.” The user also selected (represented by circle 206)a “Happy” state of mind 202, and a severity 204 of level 5 in a range of1-10 (e.g., 1 being a minimum severity level and 10 being a maximumseverity level). The text 200, the state of mind 202, and/or theseverity 204 may be transmitted to the cloud-based computing system 116via the network 112.

The cloud-based computing system 116 may receive the text 200, the stateof mind 202 of the user at the time the user entered the text 200,and/or the severity 204 of the state of mind 202 of the user. Thecloud-based computing system may use the text 200, the state of mind 202of the user at the time the user entered the text 200, and/or theseverity 204 of the state of mind 202 of the user to train a machinelearning model. For example, the machine learning model may use the textto identify a pattern between the state of mind 202 and/or the severity204 of the state of mind with the types of words included in the text, anumber of words included in the text 200, a number of misspelled wordsin the text 200, a number of pronouns in the text 200, a ratio ofpronoun count to other words in the text 200, a ratio of certain typesof words to other certain types of words in the text 200, and so forth.

For example, if the user does not use the pronoun “I”, then the user maybe trying to distance their self from a certain event described in thetext 200 and that may be a sign that the user issad/guilty/mad/regretful about the event. If the user uses certaingracious and/or celebratory words like “thankfully”, then the user maybe relieved/happy/satisfied/excited about the event they are describing.As depicted, the user entered “We” when referring to talking to an eyewitness, so the user is associating their self and someone else with theevent. Further, the user used the word “Thankfully” in the text 200, sothe user is expressing gratefulness associated with the event.

Also, the user entered 59 words, misspelled 0 words, and used twopronouns (“we” and “who”), as depicted in box 210. Further, the userindicated their state of mind 202 is “Happy” and the severity level 204of their happiness is a 5. The cloud-based computing system 116 may usethis information with the text 200 to train the machine learning models154 that these properties (e.g., word count, number of misspelled words,number of pronouns, ratio of certain words to other certain words,celebratory or gracious words, regretful words, and the like) of thestrings of characters in the text 200 (which may be entered by the useror extracted from spoken words in a video of the user) are indicative ofa pattern correlated with a “Happy” state of mind 202 and a severity 204of a level 5 for the state of mind 202 (shown in box 212).

It should be noted that numerous users may use numerous computingdevices 102 to enter any suitable text 200 with their state of mind 202at the time they entered the text and/or the severity 204 of their stateof mind 202 when the entered the text 200. The text 200 entered by themultiple users, along with the associated state of minds 202 and/orseverity 204 of the state of mind 202, may be used as training data totrain the machine learning models 154.

FIG. 3 illustrates an example block diagram of receiving additional text300 of the user according to certain embodiments of this disclosure. Asdepicted, the user is using the computing device 102 and a third partyapplication 107 that may be implementing a text input box of the API135. In the depicted example, it should be understood that the machinelearning models 154 of the cloud-computing device 116 may be trainedbased on the training data discussed above (e.g., with reference toFIGS. 1 and 2). The user of the computing device 102 is also a policeofficer filling out an incident report. The user typed “Homicide.Grusome Sceen.” The text 300 may be transferred in real-time as the usertypes each letter or after the user completes the text 300 and selectsto save the text 300 or a threshold period of time elapses since theuser's last keystroke.

The cloud-based computing system 116 may input the received text 300into the trained machine learning models 154. The trained machinelearning models 154 may determine that the text 300 includes 3 words(very low) total, 2 misspelled words (“Grusome” and “sceen”), and 0pronouns, as shown in box 306. Accordingly, the misspelling of words maybe determined to be correlated with the user using a drug or moodaltering substance (e.g., alcohol, marijuana, etc.) to try to enhancetheir mood. The low word count indicates the user is not very interestedin talking about the event they are describing. The lack of pronouns mayindicate that the user is distancing their self from the event. Further,the substance of the event (e.g., “Homicide”) and the description of theevent (e.g., “Grusome sceen”) may indicate the user experienced sometype of trauma. Further, the strings of characters in the text 300 maybe compared to other strings of characters to identify similarities ofthe strings of characters with the other strings of characters that areindicative of a particular state of mind. For example, the string ofcharacters “sad” may indicate the state of mind “sad”. Based on theforegoing, the machine learning models 154 may be trained to output adetermination that the user has a “Sad” state of mind and a severity of“7”.

Accordingly, the cloud-based computing device 116 may perform anintervention 308. The intervention 308 may be transmitted by thecloud-based computing system 116 through the network to the computingdevice 101, the computing device 102, or both. For example, theintervention 308 may be a notification to the user of the computingdevice 102 to take a break, take a walk, breathe deeply, meditate, orthe like. In another example, the intervention 308 may include notifyingthe user of the computing device 101 to reach out to the user of thecomputing device 102 to check on them. Accordingly, one or moreinterventions may be performed at an opportune time (e.g., in real-time)when a poor state of mind of the user is detected.

In some embodiments, if the user of the computing device 102 in FIG. 3is the same as the user of the computing device 102 in FIG. 2, and theamount of time between incident reports is than a threshold amount oftime, then a more sever intervention may be performed. For example, ifthe user entered text 300 one day after the user entered text 200, thenthe intervention 308 may include contacting emergency services and/or amanager of the user to indicate that a harmful event may occur to theuser or to someone else. If the severity of the state of mind of theuser gradually gets worse over a period of time, the type ofinterventions may increase from minor to major over that period of timedepending on the severity level.

FIG. 4 illustrates an example block diagram of preforming one or moreinterventions 308 according to certain embodiments of this disclosure.Continuing with the example described with reference to FIG. 3, wherethe machine learning models 154 of the cloud-based computing device 116determined the state of mind of the user is “Sad” and has a severitylevel of “7”. One or more interventions 308 may be performed. Forexample, the intervention 308 may cause a notification 400 to bepresented on the computing device 102 of the user. The notification 400may instruct the user to stand up, walk, breathe deeply, and/or brieflymeditate. Further, the intervention 302 may cause the computing device102 to connect (e.g., via cellular or WiFi) to a telephonic-healthservice. In some embodiments, the intervention 308 may change a property(block 404) of the computing device 102. For example, the propertychange may be a reduction in blue light. Blue light may delay productionof melatonin for a certain amount of time (e.g., 4 hours), thuslessening the light may enable the user to fall asleep faster and obtainbetter rest, which may enhance their mental state.

In some embodiments, the intervention 308 may include causing anotification 406 to be presented on the computing device 101. Thenotification 406 may recommend that the user of the computing device 101contacts the user X of the computing device 102. The user of thecomputing device 101 may be a loved one, family member, sponsor, friend,etc. The notification 406 may cause the user to call, text, email,and/or meet with the user of the computing device 102 to check on howthey are feeling. Another intervention 308 may include a notification408 to a user of the computing device 101. The notification 408 mayindicate that a potentially harmful event is about to occur. In someembodiments, notification 408 is transmitted when a major interventionis determined to be performed. The notification 408 may be transmittedto a computing device 101 of an emergency services user.

In some embodiments, the intervention 308 may include changing aproperty 410 of the electronic device 150. The electronic device 150 maybe a smart light, smart home hub, smart thermostat, smart speaker, smartdoorbell, and the like. For example, changing the property 410 mayinclude altering a color of light and/or brightness of the light beingemitted by the electronic device 150. In some embodiments, the change inproperty 410 may be causing a smart speaker to play relaxing music,classical music, or any music that is preferred by the user. In someembodiments, the change in property 410 may include lowering atemperature of a smart thermostat to try to cool down the user. Anysuitable change in property of an electronic device is contemplated foran intervention 308. It should be understood that the interventionsdescribed herein may be performed in real-time as the machine learningmodels 154 determine the state of mind of the user and/or a severity ofthe state of mind of the user.

FIG. 5 illustrates an example block diagram of receiving text 500 fromspoken words in a video 502 according to certain embodiments of thisdisclosure. The video 502 may be playing via the third party application107, which may be a social media website or any suitable video playerapplication. In the video 502, the user says (in bubble 504) “I'm goingto jump off a bridge.” The audio of the video may be processed usingnatural language processing to digitize the string of charactersrepresenting words spoken by the user by the cloud-based computingsystem 116 or the tracking application 111 installed and monitoring theapplications running on the computing device 102. Further, facialrecognition software may be used to determine an identity of the userthat spoke the words in the video 502. If the cloud-based computingsystem 116 extracts the strings of characters, then the video may betransmitted to the cloud-based computing system 116 for textualextraction. If the tracking application on the computing device 102extracts the strings of characters, then the strings of characters maybe transmitted to the cloud-based computing system 116. In eitherembodiment, the cloud-based computing system 116 may obtain the stringsof characters representing the words spoken in the video 502.

The cloud-based computing system 116 may input the text 500 includingthe strings of characters into the machine learning models 154. Box 512shows that the machine learning models 154 determined that the text 500includes 7 words, 0 misspelled words, and 1 pronoun. The machinelearning models 154 may use natural language processing to determinethat the substance of the phrase “I'm going to jump off a bridge” is anindication that the user in the video 502 may attempt to hurt theirself. Accordingly, the machine learning models 154 may determine thatthe state of mind of the user is “Sad” and the severity is a maximumlevel of 10, as shown in box 510.

FIG. 6 illustrates example operations of a method 600 for performing anintervention based on a state of mind of a user and a severity of mindof the user according to certain embodiments of this disclosure. Themethod 600 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software, or a combinationof both. The method 600 and/or each of their individual functions,subroutines, or operations may be performed by one or more processors ofa computing device (e.g., any component (server 128, API 135, trainingengine 152, machine learning models 154, etc.) of cloud-based computingsystem 116 of FIG. 1) implementing the method 600. The method 500 may beimplemented as computer instructions stored on a memory device andexecutable by the one or more processors. In certain implementations,the method 600 may be performed by a single processing thread.Alternatively, the method 600 may be performed by two or more processingthreads, each thread implementing one or more individual functions,routines, subroutines, or operations of the methods.

At block 602, a processing device may receive one or more strings ofcharacters composed by the user. In some embodiments, the strings ofcharacters may be composed by the user in the third party application107 and/or spoken by the user in a video playing in the third partyapplication 107.

At block 604, the processing device may determine the state of mind ofthe user by processing the one or more strings of characters. The stateof mind may include happy, sad, angry, mad, giddy, playful, silly,anxious, worried, scared, surprised, or some combination thereof.Processing the one or more strings of characters may include using oneor more machine learning models trained to determine the state of mindof the user. The machine learning models may be trained using input data(training data) including the other strings of characters composed byother users and feedback from the other users indicating states of mindof the other users when the other users composed the other strings ofcharacters. In some embodiments, the machine learning model may betrained using strings of characters entered by the user and feedbackfrom the user indicating the state of mind of the user and the severityof the state of mind of the user at the time the user composed thestrings of characters.

Processing the one or more strings of characters may include identifyingsimilarities of the one or more strings of characters with other stringsof characters indicative of the state of mind. Further, processing theone or more strings of characters to determine the state of mind of theuser may include determining properties of the strings of characters,such as word count, misspelled words, types of words used, ratios oftypes of words used relative to other words used, number of pronounsused, gracious and/or celebratory words, derogatory words, angry words,sad words, happy words, and so forth. Further, in some embodiments, theprocessing device may be communicatively coupled to an electronicmedical records system and/or financial system where, provided the usergives consent, the processing device may obtain medical informationand/or financial information pertaining to the user. The medicalinformation and/or financial information may also be used to determinethe state of mind of the user.

At block 606, the processing device may determine, based on the one ormore strings of characters, a severity of the state of mind of the user.The severity may be determined using a scale ranging from one to ten,where one is the least severe level and ten is the most severe level forthe state of mind.

At block 608, the processing device may perform an intervention, inreal-time or near real-time, based on the state of mind of the userand/or the severity of the state of mind of the user. The interventionmay include: (i) causing a color of a display screen of a computingdevice of the user to be altered, (ii) transmitting a first message to acomputing device of a third party, where the first message recommendscontacting the user, (iii) causing a prompt to be presented on thecomputing device of the user, where the prompt recommends the user tostand up, walk, breathe deeply, and/or meditate, (iv) causing thecomputing device of the user to connect to a telephonic-health service,(v) transmitting a second message to an emergency service, where thesecond message indicates an event is likely to occur, (vi) causing anelectronic device to change a property, or some combination thereof.

The processing device may also track the state of mind and/or theseverity of the state of mind of the user over time by monitoringhistorical states of mind of the user and/or severities of states ofmind of the user to determine changes in the state of mind of the userand/or severities of states of mind of the user. If a substantial change(e.g., 2-8 severities levels, occur less than a threshold period of time(e.g., within minutes, hours, days, etc.), then a major intervention mayoccur. If the changes are subtle over a period of time, then theinterventions may increase subtly or decrease subtly over time.

FIG. 7 illustrates an example computer system 700, which can perform anyone or more of the methods described herein. In one example, computersystem 700 may correspond to the computing device 101, the computingdevice 102, one or more servers 128 of the cloud-based computing system116, the electronic device 150, or one or more training engines 152 ofthe cloud-based computing system 16 of FIG. 1. The computer system 700may be capable of executing the user interface 105, the trackingapplication 111, or the third party application 107 of FIG. 1. Thecomputer system 700 may be connected (e.g., networked) to other computersystems in a LAN, an intranet, an extranet, or the Internet. Thecomputer system 700 may operate in the capacity of a server in aclient-server network environment. The computer system 700 may be apersonal computer (PC), a tablet computer, a laptop, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), asmartphone, a camera, a video camera, or any device capable of executinga set of instructions (sequential or otherwise) that specify actions tobe taken by that device. Further, while only a single computer system isillustrated, the term “computer” shall also be taken to include anycollection of computers that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of the methodsdiscussed herein.

The computer system 700 includes a processing device 702, a main memory704 (e.g., read-only memory (ROM), solid state drive (SSD), flashmemory, dynamic random access memory (DRAM) such as synchronous DRAM(SDRAM)), a static memory 706 (e.g., solid state drive (SSD), flashmemory, static random access memory (SRAM)), and a data storage device708, which communicate with each other via a bus 710.

Processing device 702 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 702 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 702 may also be one or more special-purpose processingdevices such as an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 702 is configuredto execute instructions for performing any of the operations and stepsdiscussed herein.

The computer system 700 may further include a network interface device712. The computer system 700 also may include a video display 714 (e.g.,a liquid crystal display (LCD) or a cathode ray tube (CRT)), one or moreinput devices 716 (e.g., a keyboard and/or a mouse), and one or morespeakers 718 (e.g., a speaker). In one illustrative example, the videodisplay 714 and the input device(s) 716 may be combined into a singlecomponent or device (e.g., an LCD touch screen).

The data storage device 716 may include a computer-readable medium 720on which the instructions 722 (e.g., implementing the applicationprogramming interface 135, the user interface 105, the trackingapplication 111, the third party application 107, and/or any componentdepicted in the FIGURES and described herein) embodying any one or moreof the methodologies or functions described herein are stored. Theinstructions 722 may also reside, completely or at least partially,within the main memory 704 and/or within the processing device 702during execution thereof by the computer system 700. As such, the mainmemory 704 and the processing device 702 also constitutecomputer-readable media. The instructions 722 may further be transmittedor received over a network via the network interface device 712.

While the computer-readable storage medium 720 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments, including bothstatically-based and dynamically-based equipment. In addition, theembodiments disclosed herein can employ selected equipment such thatthey can identify individual users and auto-calibrate thresholdmultiple-of-body-weight targets, as well as other individualizedparameters, for individual users.

1. A method for determining a state of mind of a user, comprising:receiving one or more strings of characters composed by the user;determining, by a processing device executing a machine learning model,the state of mind of the user by processing the one or more strings ofcharacters, wherein the processing of the one or more strings ofcharacters comprises identifying similarities of the one or more stringsof characters with other strings of characters indicative of the stateof mind, and the machine learning model is trained to determine thestate of mind of the user using input data comprising: (i) the otherstrings of characters composed by other users, and (ii) feedback enteredby the other users via user interfaces presented on computing devices ofthe other users, and the feedback comprises: an indication of currentstates of minds of the other users at the time at which the other userscomposed the other strings of characters, and an indication ofseverities of the current states of minds of the other users at the timeat which the other users composed the other strings of characters; anddetermining, based on the one or more strings of characters, a severityof the state of mind of the user.
 2. The method of claim 1, furthercomprising performing an intervention, in real-time or near real-time,based on the state of mind of the user and the severity of the state ofmind of the user.
 3. The method of claim 2, wherein the interventioncomprises: causing a color of a display screen of a computing device ofthe user to be altered; transmitting a first message to a computingdevice of a third party, wherein the first message recommends contactingthe user; causing a prompt to be presented on the computing device ofthe user, wherein the prompt recommends the user stand up, walk, orbriefly meditate; causing the computing device of the user to connect toa telephonic-health service; transmitting a second message to anemergency service, wherein the second message indicates an event islikely to occur; causing an electronic device to change a property; orsome combination thereof.
 4. (canceled)
 5. (canceled)
 6. The method ofclaim 1, wherein the state of mind is happy or sad.
 7. The method ofclaim 1, wherein the severity is determined using a scale ranging fromone to ten, one being least severe and ten being most severe.
 8. Themethod of claim 1, further comprising tracking the state of mind of theuser over time by monitoring historical states of mind of the user todetermine changes in the state of mind of the user.
 9. A tangible,non-transitory computer-readable medium storing instructions that, whenexecuted by a processing device, cause the processing device to: receiveone or more strings of characters composed by the user; determine, by aprocessing device executing a machine learning model, the state of mindof the user by processing the one or more strings of characters, whereinthe processing of the one or more strings of characters comprisesidentifying similarities of the one or more strings of characters withother strings of characters indicative of the state of mind, and themachine learning model is trained to determine the state of mind of theuser using input data comprising: (i) the other strings of characterscomposed by other users, and (ii) feedback entered by the other usersvia user interfaces presented on computing devices of the other users,and the feedback comprises: an indication of current states of minds ofthe other users at the time at which the other users composed the otherstrings of characters, and an indication of severities of the currentstates of minds of the other users at the time at which the other userscomposed the other strings of characters; and determine, based on theone or more strings of characters, a severity of the state of mind ofthe user.
 10. The computer-readable medium of claim 9, wherein theprocessing device is further to perform an intervention based on thestate of mind of the user and the severity of the state of mind of theuser.
 11. The computer-readable medium of claim 10, wherein theintervention comprises: causing a color of a display screen of acomputing device of the user to be altered; transmitting a first messageto a computing device of a third party, wherein the first messagerecommends contacting the user; causing a prompt to be presented on thecomputing device of the user, wherein the prompt recommends the userstand up, walk, or briefly meditate; causing the computing device of theuser to connect to a telephonic-health service; transmitting a secondmessage to an emergency service, wherein the second message indicates anevent is likely to occur; causing an electronic device to change aproperty; or some combination thereof.
 12. (canceled)
 13. (canceled) 14.The computer-readable medium of claim 9, wherein the state of mind ishappy or sad.
 15. The computer-readable medium of claim 9, wherein theseverity is determined using a scale ranging from one to ten, one beingleast severe and ten being most severe.
 16. The computer-readable mediumof claim 9, further comprising tracking the state of mind of the userover time by monitoring historical states of mind of the user todetermine changes in the state of mind of the user.
 17. A system,comprising: a memory device storing instructions; a processing devicecommunicatively coupled to the memory device, wherein the processingdevice executes the instructions to: receive one or more strings ofcharacters composed by the user; determine, by a processing deviceexecuting a machine learning model, the state of mind of the user byprocessing the one or more strings of characters, wherein the processingof the one or more strings of characters comprises identifyingsimilarities of the one or more strings of characters with other stringsof characters indicative of the state of mind, and the machine learningmodel is trained to determine the state of mind of the user using inputdata comprising: (i) the other strings of characters composed by otherusers, and (ii) feedback entered by the other users via user interfacespresented on computing devices of the other users, and the feedbackcomprises: an indication of current states of minds of the other usersat the time at which the other users composed the other strings ofcharacters, and an indication of severities of the current states ofminds of the other users at the time at which the other users composedthe other strings of characters; and determine, based on the one or morestrings of characters, a severity of the state of mind of the user. 18.The system of claim 17, wherein the processing device is further toperform an intervention based on the state of mind of the user and theseverity of the state of mind of the user.
 19. The system of claim 17,wherein the intervention comprises: causing a color of a display screenof a computing device of the user to be altered; transmitting a firstmessage to a computing device of a third party that recommendscontacting the user; causing a prompt to be presented on the computingdevice of the user, wherein the prompt recommends the user stand up,walk, or briefly meditate; causing the computing device of the user toconnect to a telephonic-health service; transmitting a second message toan emergency service, wherein the second message indicates an event islikely to occur; causing an electronic device to change a property; orsome combination thereof.
 20. (canceled)
 21. The method of claim 1,further comprising: determining whether the state of mind of the usersatisfies a threshold level; and responsive to determining the state ofmind of the user satisfies the threshold level, performing anintervention corresponding to the threshold level.
 22. The method ofclaim 1, further comprising: determining a difference in the severity ofthe state of mind of the user and a previously determined severity ofthe state of mind of the user; determining whether the differencesatisfies a threshold; and responsive to determining the differencesatisfies the threshold, performing an intervention based at least onone or more of the state of mind of the user, the severity of the stateof mind of the user, and the difference.